Academic staff

A list of AIAI academic staff

Member Interests

Jacques Fleuriot   

Director of Institute

My main field of research lies in AI Modelling, which spans areas such as interactive theorem proving, formal verification, process modelling, and machine learning, with an emphasis on explainable models, applied to healthcare and other complex domains.


Stefano Albrecht

Goal-directed sequential decision making by autonomous systems in complex dynamic environments. Reasoning about the beliefs, intentions, and behaviours of other decision makers. Innovative applications in areas such as cyber security and self-driving vehicles.
Paul Anderson Configuration and management of large computing infrastructures, Semantics and usability of configuration languages, Autonomics and intelligent approaches to configuration deployment, Systems administrations.
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Pavlos Andreadis I am a Teacher in Informatics primarily focussing on Applied Machine Learning. Main interest is in Recommender Systems as a tool for Collaboration and Coordination. Educated in Operations Research and Production and Management Engineering. Honoured to be the runner-up Supervisor of the Year 2020, as voted for by the Edinburgh University Students' Association (EUSA).
Malcolm Atkinson The design of data-intensive systems and languages, including EDIM1 and DISPEL, and the exploitation of data-intensive methods to discover new knowledge and improve decisions.
Vaishak Belle Explainable AI, scalable probabilistic inference and learning, probabilistic programming, statistical relational learning, commonsence reasoning, automated planning, and unifying logic and probability more generally.
Alan Bundy The automatic construction, analysis and evolution of representations of knowledge and the automation of mathematical reasoning, with applications to reasoning about the correctness of computer software and hardware.
Kobi Gal Artificial Intelligence, Machine Learning for Human-Computer Collaboration and Negotiation, Big data in Education, Plan and Goal Recognition, Collaborative Group Learning, Incentive Design for effective teamwork, Computational Cognitive Science, Intelligent and Adaptive Tutoring Systems, Computational Game Theory.
Nadin Kokciyan
Nadin Kokciyan AI for Privacy, Explainable AI, AI & Ethics, Multiagent systems, Knowledge Representation and Reasoning, Agreement Technologies
T Ma
Tiejun Ma Dr Ma’s research focuses on risk analysis and decision-making using quantitative modelling and real-time Big data analysis techniques applies to fintech, cyber-risk, and resilience. He employs applied data science and mathematical modelling methodologies in the analysis/forecasts of risks, using an inter-disciplinary research strategy, via state-of-the-art computing, data analytics and behavioural analysis techniques.
Ursula Martin
Ursula Martin

The context and culture of mathematics and foundational research, with the goal of building software to better support mathematicians, and of understanding and expanding the nature of impact.  A recently published popular book examines the scientific and mathematical education of Ada Lovelace.

Petros Papapanagiotou AI for machine-mediated collaboration; formal verification and theorem proving; process modelling and workflow management; choreography and orchestration of distributed agent systems; applications in healthcare, including integrated care pathways and healthcare data management.
Valerio Restocchi
Valerio Restocchi Modelling and simulation of complex socio-economic systems, Interaction and propagation on social networks, Modelling of people's behaviour, Financial markets and Econophysics, Cryptocurrencies and Fintech, Agent-based simulations, Network science.
Dave Robertson Design and deployment of multi-agent systems; large-scale, automated design and transformation of knowledge bases and problem solvers; agent-orientated software engineering; social computation.
Michael Rovatsos Ethical, human-friendly and responsible AI, multiagent systems, social computation.
Siddharth N.

My research broadly involves the confluence of computer vision,  robotics, natural-language processing, cognitive science, and elements  of neuroscience. It seeks to better understand perception and cognition  with a view to enabling human-intelligible machine intelligence through  learning structured and interpretable representations of perceptual  data. 

Explainable AI; Interpretable ML; Probabilistic Programming;  Approximate Inference; Human-Machine Interaction; Neural-Symbolic  Systems; Computer Vision; NLP